|
3186 | 3186 | "desc" :"Plotly chart creation", |
3187 | 3187 | "file" :"m_visualize/Plotly", |
3188 | 3188 | "apps" : { |
3189 | | -"color":5, |
| 3189 | +"color":6, |
3190 | 3190 | "icon":"apps/apps_visualize.svg" |
3191 | 3191 | } |
3192 | 3192 | }, |
|
3200 | 3200 | "desc" :"Word Cloud", |
3201 | 3201 | "file" :"m_visualize/WordCloud", |
3202 | 3202 | "apps" : { |
3203 | | -"color":5, |
| 3203 | +"color":6, |
3204 | 3204 | "icon":"apps/apps_wordcloud.svg" |
3205 | 3205 | } |
3206 | 3206 | } |
|
3226 | 3226 | "desc" :"Data sets for machine learning", |
3227 | 3227 | "file" :"m_ml/DataSets", |
3228 | 3228 | "apps" : { |
3229 | | -"color":6, |
| 3229 | +"color":11, |
3230 | 3230 | "icon":"apps/apps_dataset.svg" |
3231 | 3231 | } |
3232 | 3232 | }, |
|
3240 | 3240 | "desc" :"Data split for machine learning", |
3241 | 3241 | "file" :"m_ml/dataSplit", |
3242 | 3242 | "apps" : { |
3243 | | -"color":6, |
| 3243 | +"color":11, |
3244 | 3244 | "icon":"apps/apps_datasplit.svg" |
3245 | 3245 | } |
3246 | 3246 | }, |
|
3254 | 3254 | "desc" :"Data preparation for machine learning", |
3255 | 3255 | "file" :"m_ml/DataPrep", |
3256 | 3256 | "apps" : { |
3257 | | -"color":6, |
| 3257 | +"color":11, |
3258 | 3258 | "icon":"apps/apps_dataprep.svg" |
3259 | 3259 | } |
3260 | 3260 | }, |
|
3268 | 3268 | "desc" :"AutoML model for machine learning", |
3269 | 3269 | "file" :"m_ml/AutoML", |
3270 | 3270 | "apps" : { |
3271 | | -"color":6, |
| 3271 | +"color":11, |
3272 | 3272 | "icon":"apps/apps_automl.svg" |
3273 | 3273 | } |
3274 | 3274 | }, |
|
3282 | 3282 | "desc" :"Regression model for machine learning", |
3283 | 3283 | "file" :"m_ml/Regression", |
3284 | 3284 | "apps" : { |
3285 | | -"color":7, |
| 3285 | +"color":12, |
3286 | 3286 | "icon":"apps/apps_regression.svg" |
3287 | 3287 | } |
3288 | 3288 | }, |
|
3296 | 3296 | "desc" :"Classification model for machine learning", |
3297 | 3297 | "file" :"m_ml/Classification", |
3298 | 3298 | "apps" : { |
3299 | | -"color":7, |
| 3299 | +"color":12, |
3300 | 3300 | "icon":"apps/apps_classification.svg" |
3301 | 3301 | } |
3302 | 3302 | }, |
|
3310 | 3310 | "desc" :"Clustering model for machine learning", |
3311 | 3311 | "file" :"m_ml/Clustering", |
3312 | 3312 | "apps" : { |
3313 | | -"color":7, |
| 3313 | +"color":12, |
3314 | 3314 | "icon":"apps/apps_clustering.svg" |
3315 | 3315 | } |
3316 | 3316 | }, |
|
3324 | 3324 | "desc" :"Dimension reduction model for machine learning", |
3325 | 3325 | "file" :"m_ml/DimensionReduction", |
3326 | 3326 | "apps" : { |
3327 | | -"color":7, |
| 3327 | +"color":12, |
3328 | 3328 | "icon":"apps/apps_dimension.svg" |
3329 | 3329 | } |
3330 | 3330 | }, |
|
3338 | 3338 | "desc" :"Model save/load for machine learning", |
3339 | 3339 | "file" :"m_ml/SaveLoad", |
3340 | 3340 | "apps" : { |
3341 | | -"color":8, |
| 3341 | +"color":13, |
3342 | 3342 | "icon":"apps/apps_file.svg" |
3343 | 3343 | } |
3344 | 3344 | }, |
|
3352 | 3352 | "desc" :"Model fit/predict for machine learning", |
3353 | 3353 | "file" :"m_ml/FitPredict", |
3354 | 3354 | "apps" : { |
3355 | | -"color":8, |
| 3355 | +"color":13, |
3356 | 3356 | "icon":"apps/apps_fit.svg" |
3357 | 3357 | } |
3358 | 3358 | }, |
|
3366 | 3366 | "desc" :"Model information for machine learning", |
3367 | 3367 | "file" :"m_ml/ModelInfo", |
3368 | 3368 | "apps" : { |
3369 | | -"color":8, |
| 3369 | +"color":13, |
3370 | 3370 | "icon":"apps/apps_predict.svg" |
3371 | 3371 | } |
3372 | 3372 | }, |
|
3380 | 3380 | "desc" :"Performance evaluation for machine learning", |
3381 | 3381 | "file" :"m_ml/evaluation", |
3382 | 3382 | "apps" : { |
3383 | | -"color":8, |
| 3383 | +"color":13, |
3384 | 3384 | "icon":"apps/apps_evaluate.svg" |
3385 | 3385 | } |
3386 | 3386 | } |
|
